



'''simple docstring'''

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


SCREAMING_SNAKE_CASE__     :						Optional[int]	=  get_tests_dir('''fixtures''')

class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self						)							->							Union[str, Any]:
					# A mock response for an HTTP head request to emulate server down
					snake_case__	     =mock.Mock()
					snake_case__	     =500
					snake_case__	     ={}
					snake_case__	     =HTTPError
					snake_case__	     ={}

					# Download this model to make sure it's in the cache.
					snake_case__	     =WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'						)
					# Under the mock environment we get a 500 error when trying to reach the model.
					with mock.patch('requests.Session.request'					,   return_value=_UpperCAmelCase						) as mock_head:
							snake_case__	     =WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'						)
							# This check we did call the fake head request
							mock_head.assert_called()






			def 						_lowercase  (     self						)							->							Tuple:
					# This test is for deprecated behavior and can be removed in v5
					snake_case__	     =WavaVecaFeatureExtractor.from_pretrained(
					    'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'						)





@is_staging_test
class 							a__( unittest.TestCase     ):






			@classmethod
			def 						_lowercase  (     cls						)							->							Dict:
					snake_case__	     =TOKEN
					HfFolder.save_token(_UpperCAmelCase						)






			@classmethod
			def 						_lowercase  (     cls						)							->							List[Any]:
					try:
							delete_repo(token=cls._token					,   repo_id='test-feature-extractor'						)
					except HTTPError:
							pass

					try:
							delete_repo(token=cls._token					,   repo_id='valid_org/test-feature-extractor-org'						)
					except HTTPError:
							pass

					try:
							delete_repo(token=cls._token					,   repo_id='test-dynamic-feature-extractor'						)
					except HTTPError:
							pass






			def 						_lowercase  (     self						)							->							Tuple:
					snake_case__	     =WavaVecaFeatureExtractor.from_pretrained(_UpperCAmelCase						)
					feature_extractor.push_to_hub('test-feature-extractor'					,   use_auth_token=self._token						)

					snake_case__	     =WavaVecaFeatureExtractor.from_pretrained(f"""{USER}/test-feature-extractor"""						)
					for k, v in feature_extractor.__dict__.items():
							self.assertEqual(_UpperCAmelCase					,   getattr(_UpperCAmelCase					,   _UpperCAmelCase						)						)

					# Reset repo
					delete_repo(token=self._token					,   repo_id='test-feature-extractor'						)

					# Push to hub via save_pretrained
					with tempfile.TemporaryDirectory() as tmp_dir:
							feature_extractor.save_pretrained(
							    _UpperCAmelCase					,   repo_id='test-feature-extractor'					,   push_to_hub=_UpperCAmelCase					,   use_auth_token=self._token						)

					snake_case__	     =WavaVecaFeatureExtractor.from_pretrained(f"""{USER}/test-feature-extractor"""						)
					for k, v in feature_extractor.__dict__.items():
							self.assertEqual(_UpperCAmelCase					,   getattr(_UpperCAmelCase					,   _UpperCAmelCase						)						)






			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =WavaVecaFeatureExtractor.from_pretrained(_UpperCAmelCase						)
					feature_extractor.push_to_hub('valid_org/test-feature-extractor'					,   use_auth_token=self._token						)

					snake_case__	     =WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'						)
					for k, v in feature_extractor.__dict__.items():
							self.assertEqual(_UpperCAmelCase					,   getattr(_UpperCAmelCase					,   _UpperCAmelCase						)						)

					# Reset repo
					delete_repo(token=self._token					,   repo_id='valid_org/test-feature-extractor'						)

					# Push to hub via save_pretrained
					with tempfile.TemporaryDirectory() as tmp_dir:
							feature_extractor.save_pretrained(
							    _UpperCAmelCase					,   repo_id='valid_org/test-feature-extractor-org'					,   push_to_hub=_UpperCAmelCase					,   use_auth_token=self._token						)

					snake_case__	     =WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'						)
					for k, v in feature_extractor.__dict__.items():
							self.assertEqual(_UpperCAmelCase					,   getattr(_UpperCAmelCase					,   _UpperCAmelCase						)						)






			def 						_lowercase  (     self						)							->							str:
					CustomFeatureExtractor.register_for_auto_class()
					snake_case__	     =CustomFeatureExtractor.from_pretrained(_UpperCAmelCase						)

					feature_extractor.push_to_hub('test-dynamic-feature-extractor'					,   use_auth_token=self._token						)

					# This has added the proper auto_map field to the config
					self.assertDictEqual(
					    feature_extractor.auto_map					,   {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}					,   )

					snake_case__	     =AutoFeatureExtractor.from_pretrained(
					    f"""{USER}/test-dynamic-feature-extractor"""					,   trust_remote_code=_UpperCAmelCase						)
					# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
					self.assertEqual(new_feature_extractor.__class__.__name__					,   'CustomFeatureExtractor'						)









'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 				a  (     UpperCamelCase_    :    Any			)       ->	List[str]:
		snake_case__	     =os.path.join(args.tf_model_dir      ,					'parameters.json'			)
		snake_case__	     =json.loads(open(UpperCamelCase_			).read()			)
		if not params:
				raise ValueError(
				    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""			)
		if not args.output.endswith('.pt'			):
				snake_case__	     =args.output + '.pt'
		snake_case__	     =OrderedDict()
		with tf.device('/CPU:0'			):
				snake_case__	     =tf.train.load_checkpoint(args.tf_model_dir			)
				snake_case__	     =reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						snake_case__	     =reader.get_tensor(UpperCamelCase_			).astype(np.floataa			)
						if key_name.endswith('/adam_m'			) or key_name.endswith('/adam_v'			):
								continue
						if key_name.startswith('pasts/'			):
								if key_name.startswith('pasts/mlp'			):
										snake_case__	     =int(key_name[9]			)
								elif key_name.startswith('pasts/out'			):
										snake_case__	     =8
								snake_case__	     ='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/moe'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/switch_gating/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/softmlp/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/wo/kernel'			) or key_name.endswith('/wi/kernel'			):
										snake_case__	     =key_name[-9:-7]
										for i in range(16			):
												snake_case__	     ='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
												snake_case__	     =(
												    vnp[i].transpose([1, 0]			).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/mlp'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/p1/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p1/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wi.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/kernel'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.weight' % player
										snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/p2/bias'			):
										snake_case__	     ='model.blocks.%d.feed_forward.mlp.wo.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/ln'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.feed_forward.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/att'			):
								snake_case__	     =int(key_name[9:].split('/'			)[0]			)
								if key_name.endswith('/qkv/kernel'			):
										snake_case__	     =vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										snake_case__	     =state[:, 0, :, :]
										snake_case__	     =state[:, 1, :, :]
										snake_case__	     =state[:, 2, :, :]
										snake_case__	     =(
										    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =(
										    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/o/kernel'			):
										snake_case__	     ='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
										snake_case__	     =(
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/an'			):
								snake_case__	     =int(key_name[8:].split('/'			)[0]			)
								if key_name.endswith('/b'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.bias' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
								elif key_name.endswith('/g'			):
										snake_case__	     ='model.blocks.%d.self_attn.norm.weight' % player
										snake_case__	     =vnp.copy()  # same because it is one dimensional
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif (
						    key_name.startswith('model/wte'			)
						    or key_name.startswith('model/wpe'			)
						    or key_name.startswith('model/ete'			)
						):
								snake_case__	     ={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
								    key_name[-3:]
								]
								snake_case__	     ='model.%s.weight' % nlayer
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =torch.tensor(UpperCamelCase_			)
								if key_name.startswith('model/wte'			):
										snake_case__	     ='lm_head.weight'
										snake_case__	     =vnp.copy()  # same in embedded
										snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name.startswith('model/wob'			):
								snake_case__	     ='final_logits_bias'
								snake_case__	     =vnp.copy()  # same in embedded
								snake_case__	     =state.reshape((1, -1)			)
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense/kernel":
								snake_case__	     ='model.last_project.weight'
								snake_case__	     =vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								snake_case__	     =torch.tensor(UpperCamelCase_			)
						elif key_name == "model/dense_1/bias":
								snake_case__	     ='model.last_project.bias'
								snake_case__	     =vnp.copy()  # same because it is one dimensional
								snake_case__	     =torch.tensor(UpperCamelCase_			)
		torch.save(UpperCamelCase_      ,					args.output			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						int	=  argparse.ArgumentParser(
			    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
			)
			parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
			parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
			SCREAMING_SNAKE_CASE__     :						str	=  parser.parse_args()
			convert_tf_gptsan_to_pt(args)




